marta-sd

import tensorflow as tf


# 1. Create and save two graphs

# c = a*b
g1 = tf.Graph()
with g1.as_default():
    a = tf.placeholder(tf.float32, name='a')
    b = tf.Variable(initial_value=tf.truncated_normal((1,)), name='b')

marta-sd

from random import shuffle


def example_generator(*lists, random_order=True):
    """Create infinite generator of examples.

    example_generator([3, 6, 8], [0, 0, 1]) -> (6,0), (3,0), (8,1), (3,0)...
    """

    assert len(lists) > 0, 'no lists provided'

marta-sd

import tensorflow as tf

# 1. Create and save a network

# c = a*b
g = tf.Graph()
with g.as_default():
    a = tf.placeholder(tf.float32, name='a')
    b = tf.Variable(initial_value=tf.truncated_normal((1,)), name='b')
    c = tf.multiply(a, b, name='c')

marta-sd

from rdkit.Chem import MolFromSmiles
from decaf.toolkits.rd import phar_from_mol
from decaf.utils import similarity, combine_pharmacophores, draw, filter_nodes

from scipy.cluster.hierarchy import average as avg_clustering
from scipy.spatial.distance import squareform

smiles = [
    'c1cc(cc(c1)N)c2cc(cc(c2O)c3[nH]c4ccc(cc4n3)C(=N)N)Cl',
    'c1cc(cc(c1)N(=O)=O)c2cc(cc(c2O)c3cc4cc(ccc4[nH]3)C(=N)N)C(CC(=O)O)C(=O)O',

marta-sd

import numpy as np
import pandas as pd
import h5py

import pybel
from tfbio.data import Featurizer

import os

marta-sd

# before running this script clone Pafnucy's repository and create the environment:
# $ git clone https://gitlab.com/cheminfIBB/pafnucy
# $ cd pafnucy
# $ conda env create -f environment_gpu.yml


import numpy as np
import h5py
import tensorflow as tf

marta-sd

import torch
from torch import nn

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.l1 = nn.Sequential(nn.Linear(8, 64), nn.ReLU(), nn.Linear(64, 2))
        self.l2 = nn.Sequential(nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 2))
        self.relu = nn.ReLU()
    def forward(self, x, y):